Presented at the Transportation Research Board 2014 meeting - Past research in travel surveys has shown that a GPS mobile phone-based survey is a useful tool for collecting information about individuals. While a passive travel survey collection is preferred to an active travel survey method, passive collection remains a challenge due to a lack of high accuracy algorithms to automatically identify trip starts and trip ends. This paper presents Automatic Spatial Temporal Identication of Points of Interest (ASTIPI), an unsupervised spatial temporal algorithm to identify POIs. ASTIPI utilizes the temporal and spatial properties of the dataset to obtain a high accuracy of POI identication, even on a reduced GPS dataset that uses techniques to conserve battery life on mobile devices. While reducing outliers within POIs, ASTIPI also has a linear running time and maintains the temporal orders of the location data so that arrival and departure information can be easily extracted and thus, users' trips can be quickly identied. Using real data from mobile devices,evaluations of ASTIPI and other existing algorithms are performed, showing that ASTIPI obtains the highest accuracy of POI identication with an average accuracy of 88% when performing on full datasets generated using the GPS Auto-Sleep module and an average accuracy of 59% when performing on reduced datasets generated using both the GPS Auto-Sleep module and the Critical Points algorithm. (C) 2014 USF, Patent Pending

1. Automatic Spatial-Temporal Identification of Points
of Interest (ASTIPI) in Global Navigation Satellite
System Data
Khoa Tran1, Sean Barbeau, Ph.D.2, Miguel Labrador, Ph.D.1
Computer Science & Engineering1, Center for Urban Transportation Research 2

2. Agenda

Introduction

ASTIPI Algorithm

Evaluation

Conclusions

3. Introduction
Background
Related Works
ASTIPI
Evaluation
Introduction
Conclusions

4. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Point of Interest

Specific geographical locations that users may find
useful or interesting


Example: Home, work, restaurants, supermarket, schools, etc.
Many transportation applications
Market
School
Restaurant

5. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
GPS Dataset Properties

Property #1: Spatial temporal data.



Each coordinate is associated with a recorded time
Example: (-82.520950, 28.033525, 15m) at 09:14:54.777am
Property #2: Switching between stop and movement.

Trip re-construction can be accomplished after successfully
identifying POIs.

6. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
GPS Dataset Properties

Property #3: Noise (i.e. outliers) and the loss of data


Without clear sky view (e.g. indoors, tree cover, mountains).
POI may be represented by one or two GPS fixes, if building
blocks signal
Indoor Stationary Location

7. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
GPS Dataset Properties
Property #4: Full dataset and reduced datasets – due
to energy-saving algorithms

Sample and send less GPS fixes to conserve battery life
16
14
GPS Auto-Sleep –
12 reduces density
of clusters
10
14
Battery Life (hours)

Requirement
8
Sanyo Pro 200
6
4.21
4
GPS Auto-Sleep
& Critical Point
Algorithm –
reduces density
of clusters AND
decimates path
2
0

Impact of GPS and Wireless Tx on Battery Life for a 4-second interval
Full Dataset
Reduced Dataset

8. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
GPS Dataset Properties

Property #5: Duplicated trips

Users often travel back and forth from one place to another
using the same routes.

9. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Automatic Spatial Temporal
Identification of Points of Interest:
The Algorithm

10. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
ASTIPI

An extension of DBSCAN




Use MinTime (instead of MinPoints) to determine Core Point
Keep track of the current index to improve running time
Consider both spatial and temporal properties
Two main steps


The Main ASTIPI Algorithm
The Eps-K-Neighborhood Search

11. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Main ASTIPI Algorithm


Determine POIs given a time-ordered list of GPS fixes
Form POI by finding Core Point P and all coordinates
that are density-reachable from P or densityconnected to P


Use the Eps-K-Neighborhood Search
Maintain the index of the last coordinate in Eps-KNeighborhood of the most recent Core Point

Perform search only on necessary location data

12. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Eps-K-Neighborhood Search

Find coordinates in the trajectory that are:


Spatially close to point P
Temporally close in time to point P

Return neighbors if point P is a Core Point

Start the search from a given startIndex

Stop the search when the number of coordinates that
are outside Eps distance exceeds K

13. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Sample Execution

Parameter Values



Core Point
Coordinate inside POI
MinTime = 300 seconds
Eps = 100 meters
K=3
Coordinate outside POI
Noise
User stops for 15min
(but single GPS point)
C12
C13
C14
C11
C15
C2
C8
C1
C3
C9
C4
C7
C6
C5
C10

14. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Sample Execution

Parameter Values



Core Point
Coordinate inside POI
MinTime = 300 seconds
Eps = 100 meters
K=3
Coordinate outside POI
Noise
User stops for 15min
(but single GPS point)
C12
C13
C14
C11
C15
C2
C8
C1
C3
C9
C4
C7
C6
C5
C10

15. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Sample Execution

Parameter Values



Core Point
Coordinate inside POI
MinTime = 300 seconds
Eps = 100 meters
K=3
Coordinate outside POI
Noise
C12
C13
C14
C11
C15
C2
C8
POI1
C1
C3
C9
C4
C7
C6
C5
C10
POI2

16. Introduction
Background
Related Works
ASTIPI
Evaluation
Sample Input & Output

Input

Output
Conclusions

17. Introduction
Background
Related Works
ASTIPI
Evaluation
Evaluation
Conclusions

18. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Experimental Design


Use TRAC-IT Java ME app for data
collection, Sanyo Pro 200 mobile phone w/
assisted GPS
Ground truth: user carried the phone throughout
a day, reporting visited places at end of day (via
web interface)

Walking, driving, riding bus

19. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Experimental Design

Two different datasets:


Use GPS Auto-Sleep (Full Dataset)
Use both GPS Auto-Sleep and Critical Points Algorithm
(Reduced Dataset ~1/8th size of Full Dataset)
GPS Auto-Sleep –
reduces density
of clusters
Full Dataset
GPS Auto-Sleep
& Critical Point
Algorithm –
reduces density
of clusters AND
decimates path
Reduced Dataset

20. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Experimental Design


Accuracy = TP / (TP + FP + FN)
Evaluated against other known algorithms:





Density-Based Spatial Clustering of Applications with Noise
(DBSCAN)
Spatial Temporal (ST) DBSCAN
Clustering-Based Stops and Moves of Trajectories (CB-SMoT)
Stay Point Detection (SPD)
Fast Clustering (FC) of GNSS Data

21. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Full Dataset
100%
88%
85%
80%
Average
Accuracy
65%
60%
40%
28%
Percent of
Accuracy > 75%
26%
18%
7%
20%
7%
0%
ASTIPI
CB-SMoT
Mode
# of Tests
Stationary
Walking
Walking + Bus
Walking + Bus + Driving
Walking + Driving
Driving
2
3
3
1
14
4
SPD
ASTIPI
(%)
100
69
67
100
93
94
FC
CB-SMoT
(%)
100
39
23
13
22
15
SPD
(%)
0
46
60
63
75
82
FC
(%)
100
37
22
30
5
3

22. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Reduced Dataset
100%
80%
Average Accuracy
60%
45%
35%
40%
20%
19%
31%
31%
11%
4%
Percent of
Accuracy > 55%
11%
0%
ASTIPI
CB-SMoT
Mode
# of Tests
Stationary
Walking
Walking + Bus
Walking + Bus + Driving
Walking + Driving
Driving
2
3
3
1
14
4
SPD
ASTIPI
(%)
75
34
35
71
47
34
FC
CB-SMoT
(%)
100
24
30
43
31
24
SPD
(%)
0
31
32
63
40
36
FC
(%)
100
29
22
31
27
21

23. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
ASTIPI-With-Speed



Attempt to address low accuracy on reduced dataset
Reduces the number of False Positives
Core Point with Speed




(3) The speed at point p is less than MaxSpeed

24. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
ASTIPI-With-Speed

On dataset
with a GPS
Auto-Sleep
Module
100%
87%
80%
81%
65%
60%
Average Accuracy
40%
28%
20%
26%
18%
7%
7%
Percent of
Accuracy > 75%
0%
ASTIPI

On dataset
with a GPS
Auto Sleep
Module and
Critical Point
Algorithm
Strategy
CB-SMoT
SPD
FC
100%
80%
60%
59%
Average Accuracy
52%
35%
40%
20%
31%
31%
11%
4%
11%
0%
ASTIPI
CB-SMoT
SPD
FC
Percent of
Accuracy > 55%

25. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Execution Time

Average execution time of twenty runs on each of the test
400.00
357.05
356.38
350.00
300.00
250.00
ms
200.00
150.00
100.00
50.00
0.00
2.66
ASTIPI
0.36
CB-SMoT
SPD
FC

26. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Conclusions

27. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Summary - ASTIPI




88% of accuracy on datasets using GPS Auto-Sleep
module
59% of accuracy on reduced datasets using a
combination of GPS Auto-Sleep module and the CP
algorithm
Linear running time – O(n)
Maintains a temporal order of GPS coordinates for
fast trip segmentation

28. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Future Work

Need to improve the accuracy on reduced datasets using
the CP algorithm



Perform tests in different areas




45% and 59% are low and cannot be used in practice
Increase the number of True Positives
Different Eps and K values
More users and more data over more modes
Replace the absolute distance Eps with a relative
parameter related to the dataset
On-the-fly processing

29. Introduction
Background
Related Works
ASTIPI
Questions?
Sean J. Barbeau, Ph.D.
barbeau@cutr.usf.edu
813.974.7208
Evaluation
Conclusions

30. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions

31. Introduction
Background
Related Works
ASTIPI
Extra Slides
Evaluation
Conclusions

32. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Motivation

The rise of mobile devices and mobile apps


In 2011, 87% world population used mobile devices and 50%
American mobile users have apps
The rise of location-based apps in smartphone



74% smartphone owners use LBS
GPS and LBS devices will reach ~1,015 millions units by 2015
LBS that can personalize search and suggest places
 Extract POI from mobile users

33. Introduction
Background
Related Works
ASTIPI
Evaluation
Definitions

Trajectory Sample

Eps-K-Neighborhood

Core Point

Uses MinTime (duration) instead of MinPoints

Directly Density-Reachable

Density-Reachable

Density-Connected

Point of Interest
Conclusions

34. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Definitions - Example







Trajectory Sample = {C1, C2, …, C15}
Eps-K-Neighborhood of C3 = {C4, C5}
Core Point = {C3, C8, C11, C13}
C12 is directly density-reachable from C11
C11 is directly density-reachable from C8
C12 is density-reachable from C8
C12 and C14 are density-connected


Both are density-reachable from C8
Two points of interest
POI1
POI2

35. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Summary




Problem #1: Lack of POI identification algorithms that
have a high accuracy.
Problem #2: Lack of POI identification algorithms that
have a sufficiently high accuracy on a reduced GPS
dataset for addressing the energy consumption
problem on mobile devices.
Problem #3: Slow running time of O(n2) in worst case
scenario.
Problem #4: Unable to maintain a temporal order of
GPS fixes to support fast trip segmentation.

36. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
With GPS Auto-Sleep Module
Test
1
ASTIPI
Mode
CB-SMoT
SPD
FC
TP
FP
FN
Acc (%)
TP
FP
FN
Acc (%)
TP
FP
FN
Acc (%)
TP
FP
FN
Acc (%)
Stationary
1
0
0
100
1
0
0
100
0
1
1
0
1
0
0
100
2
Stationary
1
0
0
100
1
0
0
100
0
1
1
0
1
0
0
100
3
Walking
3
0
2
60
3
1
2
50
2
1
3
33
4
5
1
40
4
Walking
4
0
1
80
1
1
4
17
4
1
1
67
2
4
3
22
5
Walking
4
1
1
67
3
1
2
50
3
3
2
38
3
1
2
50
6
Walking + Bus
2
0
1
67
1
1
2
25
2
1
1
50
2
5
1
25
7
Walking + Bus
3
2
4
33
1
2
6
11
3
3
4
30
6
12
1
32
8
Walking + Bus
4
0
0
100
2
2
2
33
4
0
0
100
4
46
0
8
9
Walking + Bus + Driving
6
0
0
100
1
2
5
13
5
2
1
63
6
14
0
30
10
Walking + Driving
4
0
0
100
3
3
1
43
3
0
1
75
3
28
1
9
11
Walking + Driving
3
0
0
100
1
1
2
25
2
0
1
67
2
146
1
1
12
Walking + Driving
4
0
1
80
1
2
4
14
5
0
0
100
5
96
0
5
13
Walking + Driving
6
1
0
86
2
2
4
25
5
1
1
71
6
46
0
12
14
Walking + Driving
5
0
1
83
2
5
4
18
5
1
1
71
6
95
0
6
15
Walking + Driving
4
0
0
100
2
5
2
22
3
0
1
75
3
192
1
2
16
Walking + Driving
5
0
0
100
2
1
3
33
4
1
1
67
5
114
0
4
17
Walking + Driving
3
0
0
100
0
1
3
0
3
2
0
60
3
150
0
2
18
Walking + Driving
7
1
0
88
1
5
6
8
6
2
1
67
7
118
0
6
19
Walking + Driving
6
0
1
86
1
6
6
8
5
1
2
63
7
163
0
4
20
Walking + Driving
6
0
0
100
4
8
2
29
6
1
0
86
6
130
0
4
21
Walking + Driving
12
1
0
92
3
4
9
19
12
1
0
92
12
190
0
6
22
Walking + Driving
6
0
0
100
3
2
3
38
6
2
0
75
6
92
0
6
23
Walking + Driving
9
0
2
82
3
4
8
20
9
1
2
75
9
288
2
3
24
Driving
5
1
0
83
3
56
2
5
4
1
1
67
5
335
0
1
25
Driving
5
0
0
100
2
3
3
25
4
0
1
80
5
182
0
3
26
Driving
8
0
0
100
8
88
0
8
8
0
0
100
8
142
0
5
27
Driving
10
0
1
91
3
3
8
21
9
0
2
82
10
278
1
3
Average Accuracy
88%
28%
65%
18%
Percent of Accuracy>75%
85%
7%
26%
7%

37. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
With GPS Auto-Sleep Module and CP Algorithm
Test
1
ASTIPI
Mode
CB-SMoT
SPD
FC
TP
FP
FN
Acc (%)
TP
FP
FN
Acc (%)
TP
FP
FN
Acc (%)
TP
FP
FN
Acc (%)
Stationary
1
0
0
100
1
0
0
100
0
0
1
0
1
0
0
100
2
Stationary
1
1
0
50
1
0
0
100
0
0
1
0
1
0
0
100
3
Walking
1
0
4
20
1
0
4
20
1
1
4
17
1
1
4
17
4
Walking
3
1
2
50
2
1
3
33
2
3
3
25
2
2
3
29
5
Walking
2
1
3
33
1
0
4
20
3
1
2
50
2
0
3
40
6
Walking + Bus
2
1
1
50
2
1
1
50
1
2
2
20
2
5
1
25
7
Walking + Bus
2
0
5
29
3
5
4
25
3
1
4
38
6
10
4
18
8
Walking + Bus
2
4
2
25
1
3
3
14
3
4
1
38
4
9
1
23
9
Walking + Bus + Driving
5
1
1
71
3
1
3
43
5
2
1
63
6
10
1
31
10
Walking + Driving
3
1
1
60
2
2
2
33
2
2
1
40
3
7
1
27
11
Walking + Driving
2
1
1
50
2
1
1
50
2
2
1
40
2
4
1
29
12
Walking + Driving
2
1
3
33
1
2
4
14
3
2
2
43
5
2
3
29
13
Walking + Driving
5
3
1
56
1
1
5
14
5
4
1
50
6
6
3
25
14
Walking + Driving
3
1
3
43
2
3
4
22
4
2
2
50
6
5
2
36
15
Walking + Driving
3
2
1
50
2
1
2
40
3
3
1
43
3
8
1
25
16
Walking + Driving
4
4
1
44
3
6
2
27
3
6
2
27
5
15
2
15
17
Walking + Driving
2
2
1
40
2
1
1
50
2
2
1
40
3
9
0
25
18
Walking + Driving
5
4
2
45
2
6
5
15
5
4
2
45
7
7
4
21
19
Walking + Driving
6
4
1
55
3
4
4
27
5
4
2
45
7
5
3
33
20
Walking + Driving
5
7
1
38
4
8
2
29
5
7
1
38
6
15
2
19
21
Walking + Driving
10
2
2
71
8
1
4
62
9
5
3
53
12
6
2
56
22
Walking + Driving
4
3
2
44
4
9
2
27
3
7
3
23
6
12
3
17
23
Walking + Driving
6
11
5
27
5
7
6
28
6
13
5
25
9
20
6
16
24
Driving
4
6
1
36
5
14
0
26
5
6
0
45
5
34
1
10
25
Driving
3
3
2
38
1
3
4
13
3
3
2
38
5
2
2
43
26
Driving
5
9
3
29
5
4
3
42
5
8
3
31
8
20
2
21
27
Driving
6
8
5
32
5
18
6
17
6
9
5
30
10
44
5
11
Average Accuracy
45%
35%
31%
31%
Percent of Accuracy>55%
19%
11%
4%
11%

38. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
ASTIPI-With-Speed
Test
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
On dataset with a GPS Auto-Sleep Module
TP
FP
1
0
1
0
3
0
4
0
4
1
2
0
3
2
4
0
6
0
4
0
3
0
4
0
6
1
5
0
3
0
5
0
3
0
7
1
6
0
6
0
12
1
6
0
9
0
5
1
5
0
8
0
10
0
Average Accuracy
Percent of Accuracy>75%
FN
0
0
2
1
1
1
4
0
0
0
0
1
0
1
1
0
0
0
1
0
0
0
2
0
0
0
1
Acc (%)
100
100
60
80
67
67
33
100
100
100
100
80
86
83
75
100
100
88
86
100
92
100
82
83
100
100
91
87%
81%
On dataset with a GPS Auto Sleep Module and Critical
Point Algorithm Strategy
TP
1
1
1
3
2
2
2
2
5
3
2
2
5
3
3
4
2
4
4
6
9
4
7
4
3
6
6
FP
0
0
0
1
1
1
0
2
1
0
0
0
1
1
0
1
1
1
2
0
1
1
2
0
0
1
1
FN
0
0
4
2
3
1
5
2
1
1
1
3
1
3
1
1
1
3
3
0
3
2
4
1
2
2
5
Percent of Accuracy>55%
Acc (%)
100
100
20
50
33
50
29
33
71
75
67
40
71
43
75
67
50
50
44
100
69
57
54
80
60
67
50
59%
52%

39. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Execution Time

Average execution time of twenty runs on each of the dataset
Test
Size (Points)
ASTIPI (ms)
CB-SMoT (ms)
SPD (ms)
FC (ms)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
75
79
76
332
122
430
615
1489
1000
730
791
988
1086
1137
1186
1201
1222
1372
1848
1898
1964
2492
3122
2625
1199
3498
4331
0.05
0.05
0.05
1.35
0.05
1.55
2.00
5.00
2.35
2.55
1.40
1.60
1.70
2.05
2.05
2.30
2.25
2.60
3.80
3.25
3.40
4.40
6.40
4.45
1.75
5.75
7.55
2.66
0.75
0.75
0.75
0.75
0.75
21.95
44.10
258.70
113.90
63.50
73.10
114.20
136.40
151.00
162.90
166.90
171.95
217.35
409.80
429.95
450.00
726.85
1119.85
830.90
167.25
1508.35
2297.60
357.05
0.05
0.05
0.05
0.15
0.05
0.10
0.20
0.40
0.15
0.35
0.20
0.30
0.25
0.25
0.35
0.30
0.35
0.35
0.50
0.55
0.50
0.70
0.85
0.60
0.25
0.95
1.05
0.36
0.70
0.90
1.90
16.70
3.05
21.60
44.70
263.55
119.65
77.70
77.90
117.45
140.90
155.30
168.65
177.70
179.20
230.05
418.90
443.80
471.35
738.25
1154.65
817.40
174.05
1411.10
2195.05
356.38
Average

40. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions

41. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Problem Statement




Problem #1: Lack of POI identification algorithms that
have a high accuracy.
Problem #2: Lack of POI identification algorithms that
have a sufficiently high accuracy on a reduced GPS
dataset to take into consideration the dynamic
sampling and sending rates of GPS fixes, solving the
limited energy resource problem on mobile devices.
Problem #3: Slow running time of O(n2) in worst case
scenario.
Problem #4: Unable to maintain a temporal order of
GPS fixes to support fast trip segmentation.

42. Introduction
Background
Related Works
ASTIPI
Evaluation
Background
Conclusions

43. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
GPS Auto-Sleep

Goal: Save battery energy while allowing real-time
tracking through dynamic GPS sampling rates



Adjust the GPS sampling rate based on user movement
Actively moving: High sampling rate
Stationary: Low sampling rate

44. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Critical Points Algorithm

Goal: Save battery energy and user’s budget by
sending fewer GPS fixes wirelessly

Send less data over the network by filtering out GPS fixes
• Change in direction of the path
• Speed at a point

45. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Sample Datasets
Dataset Using GPS Auto-Sleep
Reduced Dataset Using Both GPS AutoSleep and Critical Points Algorithm

46. Introduction
Background
Related Works
ASTIPI
Evaluation
Related Works
Conclusions

47. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
DBSCAN

Density-Based Spatial Clustering of Applications with
Noise



Eps-Neighborhood of a point P – points within Eps distance from P
Core Point if number of neighbors > MinPoints
Density-Reachable and Density-Connected from P

48. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
DBSCAN – Limitations

Running time: O(n2)

Do not use temporal properties

Loss of location data and returning trips problem
Uses MinPoints to identify Core Point
 A POI may have only one or two GPS fixes


49. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
ST-DBSCAN

Spatial Temporal DBSCAN

Extension of DBSCAN



Discover clusters based on both spatial and temporal values
Neighbors must meet both spatial condition (Eps1) and
temporal condition (Eps2)
Limitation:

Treat spatial and temporal values separately
• Each point in the dataset is strongly correlated by space and time
• Difficult to find Eps1 and Eps2

50. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
CB-SMoT

Clustering-Based Stops and Moves of Trajectories



A two-step algorithm:





Computes stops and moves
Finds interesting places that are missing from the given
geographical locations
Step 1: Identifies potential stops
Step 2: Identifies unknown stops
Focuses on Step 1
Extension of DBSCAN which determines Core Point by
duration
Uses quantile function to obtain a relative parameter
of Eps distance

51. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
CB-SMoT – Limitations

Running time: O(n2)

Quantile function

Requires a priori knowledge of the proportion between
points inside potential stops and total points in the dataset
• This proportion varies based on user’s activities
• Mean and standard deviation of distance among coordinates vary
when a dynamic GPS sampling rate is involved

Returning trips problem

52. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Stay Point Detection

Stay Point Detection

Looks for a region where users spend a long period of time

Uses spatial temporal properties

Limitations:


Running time: O(n2)
Double error when loss of location data and GPS outliers
occur consecutively

53. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Fast Clustering

Fast Clustering of Global Navigation Satellite System
Data

Agglomerative hierarchical clustering approach
• Clusters are combined if distance is close (AVL-tree-merge)



Represents clusters by AVL trees to maintain temporal order
Memory storage: O(n)
Limitations:


Running time: O(n2logn)
No noise detection and reduction
• Introduces "pseudo-POIs”

54. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Experimental Design

GPS Auto-Sleep state values







state[3] = 64 seconds
state[4] = 150 seconds
state[5] = 256 seconds
CP Algorithm thresholds values




state[0] = 4 seconds
state[1] = 8 seconds
state[2] = 16 seconds
min speed threshold = 0.1 meters per second
max walk speed = 0.6 meters per second
angle threshold = 4.5 degrees for walk trips and 3 degrees for
car trips.
Accuracy = TP/(TP + FP + FN)

55. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Experimental Design


Use TRAC-IT Java ME app for data
collection, Sanyo Pro 200 mobile phone w/
assisted GPS
Ground truth: user carries the phone throughout
a day, reporting visited places


Walking, driving, riding bus
Compare ASTIPI with CB-SMoT, SPD, and FC
Parameters
ASTIPI
CB-SMoT
SPD
FC
MinTime
5 minutes
5 minutes
5 minutes
N/A
Eps
100 meters
100 meters
180 meters
100 meters
K
3
N/A
N/A
N/A

56. Introduction
Background
Related Works
ASTIPI
Evaluation
Conclusions
Running Time

Eps-K-Neighborhood Search

One for-loop: start from a given startIndex and halted after a
constant K times failing to meet the close proximity condition
between two coordinates
• Worst case scenario: O(n)
– The main ASTIPI algorithm also stops
– ASTIPI algorithm has a linear running time
• Other scenario:
– Stop after a constant number of comparisons
– If Core Point, ASTIPI algorithm continues with next index

Number of comparisons in ASTIPI = F(n) + C

ASTIPI runs in linear time